SeekStorm: sub-millisecond, native vector & lexical search - in-process library & multi-tenancy server, in Rust.
Development started in 2015, in production since 2020, Rust port in 2023, open sourced in 2024, work in progress.
SeekStorm is open source licensed under the Apache License 2.0
SeekStorm in-process search library
SeekStorm multi-tenancy search server
SeekStorm REST client (Rust)
SeekStorm REST client (Python)
SeekStorm REST client (TypeScript)
SeekStorm REST client (C#)
SeekStorm REST client (Java)
SeekStorm Instant search adapter
A drop-in adapter to redirect an existing Algolia InstantSearch.js frontend to a SeekStorm backend — no UI rewrite required. Or, use the InstantSearch widget library to build a fresh UI from scratch, with SeekStorm's REST API as the backend.
Website | Benchmark | Demo | Library Docs | REST API Docs | Server Readme | Client Readme | Roadmap | Blog | X
Blog Posts:
- SeekStorm is now Open Source
- SeekStorm gets Faceted search, Geo proximity search, Result sorting
- SeekStorm sharded index architecture - using a multi-core processor like a miniature data center
- N-gram index for faster phrase search: latency vs. size
- Typo-tolerant Query auto-completion - derived from indexed documents
- SeekStorm 3.0 adds vector search & hybrid search
SeekStorm high-performance search library
Hybrid search
- Internally, SeekStorm uses two separate, first-class, native index architectures for vector search and keyword search. Two native cores, not just a retrofit, add-on layer.
- SeekStorm doesn’t try to make one index do everything. It runs two native search engines and lets the query planner decide how to combine them.
- Two native index architectures under one roof:
- Lexical search: an inverted index optimized for lexical relevance,
- Vector search: an ANN index optimized for vector similarity.
- Both are first-class engines, integrated at the query planner level.
- Query planner with multiple QueryModes and FusionTypes
- Per query choice of lexical search, vector search, or hybrid search.
- Separate internal index, storage layouts, indexing, search, scoring, top-k candidates - unified query planner and result fusion (Reciprocal Rank Fusion - RRF).
- But the user is fully shielded from the complexity, as if it was only a single index.
- Enables pure lexical, pure vector or hybrid search (exhaustive, not only re-ranking of preliminary candidates).
Architecture
- Fast sharded indexing: 35K docs/sec = 3 billion docs/day on a laptop.
- Fast sharded search: 7x faster query latency, 17x faster tail latency (P99) for lexical search.
- Billion-scale index
- Index either in RAM or memory mapped files
- Cross-platform (Windows, Linux, MacOS)
- SIMD (Single Instruction, Multiple Data) hardware acceleration support,
both for x86-64 (AMD64 and Intel 64) and AArch64 (ARM, Apple Silicon). - Single-machine scalability: serving thousands of concurrent queries with low latency from a single commodity server without needing clusters or proprietary hardware accelerators.
- 100% human 😎 craftsmanship - No AI 🤖 was forced into vibe coding/AI slop.
Vector Features
- Multi-Vector indexing: both from multiple fields and from multiple chunks per field.
- Integrated inference: Generate and index embeddings from any text document field, using Model2Vec from MinishLab.
- Alternatively, import and index externally generated embeddings.
- Multiple vector precisions: F32, I8.
- Multiple similarity measures: Cosine similarity, Dot product, Euclidean distance.
- TurboQuant (TQ) and affine Scalar Quantization (SQ).
- Chunking that respects sentence boundaries and Unicode segmentation for multilingual text.
- K-Medoid clustering: PAM (Partition Around Medoids) with actual data points as centers.
- Sharded and leveled IVF index.
- Approximate Nearest Neighbor Search (A